criminology and contract cheating · criminology and contract cheating: prevalence, detection, and...

Criminology and contract cheating:

Prevalence, detection, and prevention

Joe Clare

Presentation at the 2nd Annual TEQSA Conference

Melbourne, Australia

December 1, 2017

1

Acknowledgment of co-authors across papers

Dr Guy Curtis, Associate Dean Sonia Walker, & Dr Julia Hobson – Murdoch

Dr Michael Baird – Curtin

What is this talk about?

• Exploring contract cheating from a criminological perspective

o Summarising three 2017 papers to suggest that opportunity theory has a

contribution to make

• Crime is non-random, opportunity-based explanations are the best

way to account for this, and opportunity can be adjusted to prevent

crime

• If opportunity theory is relevant, what would we expect?

o Prevalence

o Repeats (targets and perpetrators)

o Prevention

• What we found

• What does this mean2

3

Crime is non-random

It clusters at very specific spaces

4Source. Eck (2015). Who should prevent crime at places? The advantages of regulating place managers and challenges to police services. Policing

10% of addresses account for 80% of crime

5

It involves very specific offenders

10% of the population account for

66% of crime(prevalence)

Most active 10% of

offenders account for

41% of crime(frequency)

Source. Martinez, Lee, Eck, & SooHyun (2017). Ravenous wolves revisited: a systematic review of offending concentration. Crime Science

6

It involves very specific victims

Source. SooHyun, Martinez, Lee, & Eck (2017). How concentrated is crime among victims? A systematic review from 1977 to 2014. Crime Science

10% of the population experience

74% of victimisation

(prevalence)

Most victimised 10%

experienced 35% of

victimisation(frequency)

7Source: Sidebottom, A. et al. (2011). Theft in price-volatile markets: on the relationship between copper price and copper theft. Journal of Research in Crime and Delinquency, 48(3), 396-418.

It involves very specific targets

Think of it loosely like an 80:20 rule

8

• The 80:20 Rule – a small number of things are responsible for a large proportion of outcomes

o The rule-of-thumb is important to target interventions

o The percentages change (80:20) depending on the problem

o Small numbers of offenders are responsible for many crimes

o Small numbers of victims suffer a large amount of victimisation

o Small numbers of places are the locations of many crimes

• In pure terms

o Repeat-location problems (time-space interaction)

o Repeat-offender problems

o Repeat-victim problems (individual/target)

9

We can explain the non-randomness of

crime

• The problem analysis triangle helps us understand how and why crime occurs at a specific time in a specific place involving specific people/targets

http://www.popcenter.org/

The problem analysis triangle

10

• The problem analysis triangle helps us understand how and why crime occurs at a specific time in a specific place involving specific people/targets

http://www.popcenter.org/

The problem analysis triangle

11

Crime is an opportunity-based rational choice

• Decisions to offend are constrained by time, cognitive

ability and information

o Bounded rationality

o Within the context, to that person, it made sense, at the time

• “Perceptions” of the situation and of risks and rewards is

more important that actual circumstances

• Decisions vary by the different stages of the offense and

among different offenders

12

Crime is an opportunity-based rational choice

• Individuals who are not normally “criminals” may choose to

offend based on the perceived risks and rewards

• If offenders choose to commit crimes based on a number of

factors, then those factors can be altered to discourage

them from choosing to offend

• Crime – in many circumstances –

is not inevitable

13

14

We can prevent non-random, rational

events by altering the opportunity

Using these explanations to develop targeted prevention

• Effective problem solving requires understanding

o How offenders and targets come together in places (time and space)

o How offenders, targets, and places are not effectively controlled

▪ Handlers, guardians, and managers

• “Think thief” to understand why offending in that opportunity structure was ‘rational’

• Analysis in this way identifies weaknesses in the problem analysis triangle

• This will point to targeted interventions designed to address the specific problem

15

We can use this approach to reduce opportunity for crime

16

• Manipulate the opportunity structure by…

• Increasing the effort

• Increasing the risk

• Reducing the rewards

• Reducing the provocations

• Removing the excuses

• These are the 5 mechanisms that underpin the25 techniques of Situational Crime Prevention

17

18Source: https://t3.ftcdn.net/jpg/01/26/46/68/240_F_126466885_sSsDz5lzxlkXvs7Cv5CsdUELx2LeMVH5.jpg

Relating this to contact cheating

• So what would we expect for people paying a third-party to

do assessments for them?

1. Relatively few students will be doing it

2. A large number of those who are doing it are probably

repeat offenders

3. Not all assessment items are going to be suitable targets

4. Suitable targets will be repeatedly victimised

5. It should be possible to alter currently suitable opportunities

for contract cheating to make them less suitable

19

20

1. Relatively few students will be doing it

• Combined results from 5 self-report

surveys of contract cheating

• N = 1,378 respondents

• 2.1% of students reported engaging

in contract cheating

• Bretag et al., 2017, estimated 2.2%

of respondents had obtained an

assignment to submit as their own

work

21

2. Likely to be repeat offending

• Combined results from 5 self-report

surveys of contract cheating

• N = 1,378 respondents

• 63% of contract cheaters reported

doing so more than one

• Contract cheating was moderated by

opportunity

o Students who had studied longer were

more likely to have cheated

22

3. Not all assessment items will be suitable targets

• Looking for unusual difference scores

between supervised and unsupervised

assessment items

o 3,798 unit results from 1,459 students

• Unusual pattern (UP) 1

o Unsupervised ≥ 70% and Supervised ≤ 50%

• UP 2

o (Unsupervised − Supervised) ≥ 25 percentage points

• UP 3

o Unsupervised ≥ 80% and

(Unsupervised − Supervised) ≥ 40 percentage points

• UP 4

o Unsupervised ≥ 60% and Supervised ≤ 30%

• UP 5

o (Unsupervised − Supervised) ≥ 95% CI

4.6% incidence

8.1% incidence

0.7% incidence

0.7% incidence

5.0% incidence

23

3. Not all assessment items will be suitable targets

• Looking for unusual difference scores

between supervised and unsupervised

assessment items

o 3,798 unit results from 1,459 students

• Unusual pattern (UP) 1

o Unsupervised ≥ 70% and Supervised ≤ 50%

• UP 2

o (Unsupervised − Supervised) ≥ 25 percentage points

• UP 3

o Unsupervised ≥ 80% and

(Unsupervised − Supervised) ≥ 40 percentage points

• UP 4

o Unsupervised ≥ 60% and Supervised ≤ 30%

• UP 5

o (Unsupervised − Supervised) ≥ 95% CI

4.6% incidence

8.1% incidence

0.7% incidence

0.7% incidence

5.0% incidence

Just because the difference patterns are

unusual, doesn’t mean the students are

cheatingo Type 1 errors – false positives - Terrible at

exams? Potentially identified for educational

support

o Type 2 errors – missing those who do just-

enough on exams - Looking across Units

prevents one-offs

These difference patterns are a proxy for

a non-random ‘problem’

24

3. Not all assessment items will be suitable targets

0 1 2 3 4 5

CRM_ACRM_BCRM_CCRM_DCRM_ECRM_FCRM_GCRM_HLAW_ALAW_BLAW_CLAW_DLAW_ELAW_FLAW_GLAW_HLAW_ILAW_JLAW_KLAW_L

LAW_MLAW_NLAW_OLAW_PLLB_ALLB_BLLB_CLLB_DLLB_ELLB_FLLB_GLLB_HLLB_ILLB_JLLB_K

Pattern significantly greater at the unit-level• Criminology units

demonstrated

significantly more

frequent unusual

patterns often

• Law units showed

some unusual patterns

• One LLB unit showed

unusual patterns

25

31 students with multiple violations: 2.1% of population…

• Previous prevalence estimates

2.1%/2.2%

• Repeats common within this

sample across rule types

• As a proportion of units in the sample:

o Students 3, 8, 9, 14, 16, 22, 25, & 31 had UPs for 100% of units

o Students 2, 17, 23 and 27 had UPs for 3 out of 4 units

Student # UP1 UP2 UP3 UP4 UP5

# Units in

sample

1 5

2 4

3 2

4 4

5 8

6 3

7 6

8 2

9 2

10 4

11 4

12 3

13 3

14 2

15 4

16 2

17 4

18 3

19 7

20 5

21 5

22 2

23 4

24 3

25 2

26 4

27 6

28 5

29 7

30 4

31 3

% unusual

unitsLegend

= no Ups

= 1 UP

= 2 UPs

= 3 UPs

26

31 students with multiple violations: 2.1% of population…

• Previous prevalence estimates

2.1%/2.2%

• Repeats common within this

sample across rule types

• As a proportion of units in the sample:

o Students 3, 8, 9, 14, 16, 22, 25, & 31 had UPs for 100% of units

o Students 2, 17, 23 and 26 had UPs for 3 out of 4 units

Student # UP1 UP2 UP3 UP4 UP5

# Units in

sample

1 5

2 4

3 2

4 4

5 8

6 3

7 6

8 2

9 2

10 4

11 4

12 3

13 3

14 2

15 4

16 2

17 4

18 3

19 7

20 5

21 5

22 2

23 4

24 3

25 2

26 4

27 6

28 5

29 7

30 4

31 3

% unusual

unitsLegend

= no Ups

= 1 UP

= 2 UPs

= 3 UPs

27

5. If opportunity matters, altering opportunity should prevent contract cheating

Also Case Study 4 in:

28

5. If opportunity matters, altering opportunity should prevent contract cheating

• Case study from B.Commerce

student capstone unit

• Varied assessment items

o Business simulation

o Case study

o Weekly eTests

o Presentation

• Anonymous feedback (2015)

revealed contract cheating

problem relating to the parameters

for the business simulation

29

5. If opportunity matters, altering opportunity should prevent contract cheating

• Risk, reward, effort, excuses, and

provocations manipulated to adjust

the opportunity structure

• Types of changes included

o Anonymous feedback to allow

reporting

o Team ‘shake-ups’ to break up groups

o Increased variability between classes

o Increased education about academic

misconduct

• 1-year post changes

o Academic misconduct decreased

from 183 to 27 (85% decline)

o Did not hinder genuine students’

ability to succeed

What does all of this mean?

• Consistent estimates – 2.1%/2.2% students contract cheating

• Repeat offending common, moderated by opportunity

• Variability in assessment items for targeting – not all targets

‘suitable’

• Just because the difference patterns are unusual, doesn’t mean the

students are cheating

o Type 1 errors – false positives - Terrible at exams? Potentially identified for

educational support

o Type 2 errors – missing those who do just-enough on exams - Looking across Units

prevents one-offs

o These difference patterns are a proxy for a non-random ‘problem’

• When contract cheating is detected, manipulating the opportunity

structure can prevent the problem

o Not dependent on apprehension30

31

Questions…

Dr Joe Clare

Murdoch University, Western Australia

j.clare@murdoch.edu.au

+61 8 9360 2319